CC BY 4.0 UnportedStokes, P.W.Casey, M.J.E.Cocks, D.G.de Urquijo, J.García, G.Brunger, M.J.White, R.D.2025-01-282025-01-282020https://oa.tib.eu/renate/handle/123456789/18520https://doi.org/10.34657/17540We present a set of self-consistent cross sections for electron transport in gaseous tetrahydrofuran (THF), that refines the set published in our previous study [1] by proposing modifications to the quasielastic momentum transfer, neutral dissociation, ionisation and electron attachment cross sections. These adjustments are made through the analysis of pulsed-Townsend swarm transport coefficients, for electron transport in pure THF and in mixtures of THF with argon. To automate this analysis, we employ a neural network model that is trained to solve this inverse swarm problem for realistic cross sections from the LXCat project. The accuracy, completeness and self-consistency of the proposed refined THF cross section set is assessed by comparing the analyzed swarm transport coefficient measurements to those simulated via the numerical solution of Boltzmann’s equation.enghttps://creativecommons.org/licenses/by/4.0530Artificial neural networkBiomoleculeMachine learningSwarm analysisArticle